Self-Supervised Iterative Refinement for Anomaly Detection in Industrial Quality Control

TL;DR

This paper presents the Iterative Refinement Process (IRP), a novel anomaly detection method that improves defect detection accuracy in industrial quality control by cyclically refining data and models, especially in noisy environments.

Contribution

The paper introduces IRP, a new iterative data refinement approach that enhances anomaly detection accuracy and robustness in industrial quality control settings.

Findings

IRP outperforms traditional models on benchmark datasets.

IRP maintains high accuracy in noisy, real-world industrial environments.

IRP effectively manages sparse and noisy data challenges.

Abstract

This study introduces the Iterative Refinement Process (IRP), a robust anomaly detection methodology designed for high-stakes industrial quality control. The IRP enhances defect detection accuracy through a cyclic data refinement strategy, iteratively removing misleading data points to improve model performance and robustness. We validate the IRP's effectiveness using two benchmark datasets, Kolektor SDD2 (KSDD2) and MVTec AD, covering a wide range of industrial products and defect types. Our experimental results demonstrate that the IRP consistently outperforms traditional anomaly detection models, particularly in environments with high noise levels. This study highlights the IRP's potential to significantly enhance anomaly detection processes in industrial settings, effectively managing the challenges of sparse and noisy data.